---
library_name: transformers
license: mit
datasets:
- emhaihsan/quran-indonesia-tafseer-translation
language:
- id
base_model:
- Qwen/Qwen2.5-3B-Instruct
---

# Model Card for Fine-Tuned Qwen2.5-3B-Instruct

This is a fine-tuned version of the [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) model. The fine-tuning process utilized the [Quran Indonesia Tafseer Translation](https://huggingface.co/datasets/emhaihsan/quran-indonesia-tafseer-translation) dataset, which provides translations and tafsir in Bahasa Indonesia for the Quran.

## Model Details

### Model Description

- **Base Model:** [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
- **Fine-Tuned By:** Ellbendl Satria
- **Dataset:** [emhaihsan/quran-indonesia-tafseer-translation](https://huggingface.co/datasets/emhaihsan/quran-indonesia-tafseer-translation)
- **Language:** Bahasa Indonesia
- **License:** MIT

This model is designed for NLP tasks involving Quranic text in Bahasa Indonesia, including understanding translations and tafsir.

## Uses

### Direct Use

This model can be used for applications requiring the understanding, summarization, or retrieval of Quranic translations and tafsir in Bahasa Indonesia.

### Downstream Use

It is suitable for fine-tuning on tasks such as:
- Quranic text summarization
- Question answering systems related to Islamic knowledge
- Educational tools for learning Quranic content in Indonesian

### Out-of-Scope Use

This model is not suitable for general-purpose conversation or tasks unrelated to Quranic and Islamic texts.

## Bias, Risks, and Limitations

### Biases
- The model inherits any biases present in the dataset, which is specific to Islamic translations and tafsir in Bahasa Indonesia.

### Limitations
- The model is tailored for Quranic and Islamic context, and its performance outside this domain may be suboptimal.
- It may not accurately handle nuanced or non-standard interpretations of Quranic text.

### Recommendations
- Users should ensure that applications using this model respect cultural and religious sensitivities.
- Results should be verified by domain experts for critical applications.

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran-GGUF")
model = AutoModelForCausalLM.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran-GGUF")

input_text = "Apa tafsir dari Surat Al-Fatihah ayat 1?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))